Graph Learning Techniques

Baoling Shan, Xin Yuan, Wei Ni (Principal Scientist at CSIRO, Australia) , Ren Ping Liu

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English

CRC Press
26 February 2025

Combinatorics & graph theory; Data mining; Privacy & data protection; Neural networks & fuzzy systems

This comprehensive guide addresses key challenges at the intersection of data science, graph learning, and privacy preservation.

It begins with foundational graph theory, covering essential definitions, concepts, and various types of graphs. The book bridges the gap between theory and application, equipping readers with the skills to translate theoretical knowledge into actionable solutions for complex problems. It includes practical insights into brain network analysis and the dynamics of COVID-19 spread. The guide provides a solid understanding of graphs by exploring different graph representations and the latest advancements in graph learning techniques. It focuses on diverse graph signals and offers a detailed review of state-of-the-art methodologies for analyzing these signals. A major emphasis is placed on privacy preservation, with comprehensive discussions on safeguarding sensitive information within graph structures. The book also looks forward, offering insights into emerging trends, potential challenges, and the evolving landscape of privacy-preserving graph learning.

This resource is a valuable reference for advance undergraduate and postgraduate students in courses related to Network Analysis, Privacy and Security in Data Analytics, and Graph Theory and Applications in Healthcare.

By: Baoling Shan, Xin Yuan, Wei Ni (Principal Scientist at CSIRO, Australia), Ren Ping Liu, Eryk Dutkiewicz
Imprint: CRC Press
Country of Publication: United Kingdom
Dimensions: Height: 234mm, Width: 156mm,
Weight: 453g
ISBN: 9781032851136
ISBN 10: 1032851139
Pages: 162
Publication Date: 26 February 2025
Audience: College/higher education , Professional and scholarly , A / AS level , Further / Higher Education
Format: Hardback
Publisher's Status: Active

Table of Contents Abstract List of Figures List of Tables Contributors 1. Introduction 2. Privacy Considerations in Graph and Graph Learning 3. Existing Technologies of Graph Learning 4. Graph Extraction and Topology Learning of Band-limited Signals 5. Graph Learning from Band-Limited Data by Graph Fourier Transform Analysis 6. Graph Topology Learning of Brain Signals 7. Graph Topology Learning of COVID-19 8. Preserving the Privacy of Latent Information for Graph-Structured Data 9. Future Directions and Challenges 10. Appendix Bibliography Index

Baoling Shan is currently a Lecturer at University of Science and Technology Beijing, Beijing, China. Xin Yuan is currently a Senior Research Scientist at CSIRO, Sydney, NSW, Australia, and an Adjunct Senior Lecturer at the University of New South Wales. Wei Ni is a Principal Research Scientist at CSIRO, Sydney, Australia, a Fellow of IEEE, a Conjoint Professor at the University of New South Wales, an Adjunct Professor at the University of Technology Sydney, and an Honorary Professor at Macquarie University. Ren Ping Liu is a Professor and the Head of the Discipline of Network and Cybersecurity, University of Technology Sydney (UTS), Ultimo, NSW, Australia. Eryk Dutkiewicz is currently the Head of School of Electrical and Data Engineering at the University of Technology Sydney, Australia. He is a Senior Member of IEEE and his research interests cover 5G/6G and IoT networks.